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                <p>Tensorflow的部分知识点<span id="more"></span></p>
<h1 id="一、placeholder"><a href="#一、placeholder" class="headerlink" title="一、placeholder"></a>一、placeholder</h1><p>&emsp;&emsp;<strong>简述：</strong>placeholder也是一个tensor,tensorflow在运行时动态的设置某个变量的值。先使用placeholder占位。只能在图计算时，用feed_dict的字典给数据。函数定义如下：</p>
<pre class="line-numbers language-python"><code class="language-python">tf<span class="token punctuation">.</span>placeholder<span class="token punctuation">(</span>
    dtype<span class="token punctuation">,</span>
    shape<span class="token operator">=</span>None<span class="token punctuation">,</span>
    name<span class="token operator">=</span>None
<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><strong>参数详解：</strong></p>
<ul>
<li>dytpe: 被填充的张量的元素类型，如常见的float32。</li>
<li>shape: 可选参数。</li>
<li>name: 可选参数。   如：x=tf.placeholder(tf.float32,shape=(1024,1024))。</li>
</ul>
<p><strong>示例如下：</strong><br>（注：运行程序时，用feed_fict的方式把具体的值提供给placeholder，达到给计算图提供input的目的。）</p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf

a <span class="token operator">=</span> tf<span class="token punctuation">.</span>placeholder<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> name <span class="token operator">=</span> <span class="token string">"input_1"</span><span class="token punctuation">)</span>
b <span class="token operator">=</span> tf<span class="token punctuation">.</span>placeholder<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">,</span> name <span class="token operator">=</span> <span class="token string">"input_2"</span><span class="token punctuation">)</span>
output <span class="token operator">=</span> tf<span class="token punctuation">.</span>multiply<span class="token punctuation">(</span>a<span class="token punctuation">,</span> b<span class="token punctuation">,</span> name <span class="token operator">=</span> <span class="token string">"mul_out"</span><span class="token punctuation">)</span>

input_dict <span class="token operator">=</span> <span class="token punctuation">{</span>a <span class="token punctuation">:</span> <span class="token number">7.0</span><span class="token punctuation">,</span> b <span class="token punctuation">:</span> <span class="token number">10.0</span><span class="token punctuation">}</span>

<span class="token keyword">with</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">as</span> sess<span class="token punctuation">:</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span>sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>output<span class="token punctuation">,</span> feed_dict <span class="token operator">=</span> input_dict<span class="token punctuation">)</span><span class="token punctuation">)</span> <span class="token comment" spellcheck="true">#feed_dict是一个字典结构</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><strong>输出结果</strong>：</p>
<p><img src="/Images/Tensorflow2/image-20210405162626675.png" alt="image-20210405162626675"></p>
<h1 id="二、tf-nn-embedding-lookup-的用法"><a href="#二、tf-nn-embedding-lookup-的用法" class="headerlink" title="二、tf.nn.embedding_lookup()的用法"></a>二、tf.nn.embedding_lookup()的用法</h1><p>&emsp;&emsp;<strong>简述：</strong>nn.embedding_lookup就是按照ids顺序返回params中的第ids行。选取一个张量中索引对应的元素。函数定义如下：</p>
<pre class="line-numbers language-python"><code class="language-python">tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>embedding_lookup<span class="token punctuation">(</span>
    params<span class="token punctuation">,</span>
    ids<span class="token punctuation">,</span>
    partition_strategy <span class="token operator">=</span> <span class="token string">'mod'</span><span class="token punctuation">,</span>
    name <span class="token operator">=</span> None<span class="token punctuation">,</span>
    validate_indices <span class="token operator">=</span> <span class="token boolean">True</span><span class="token punctuation">,</span>
    max_norm <span class="token operator">=</span> None
<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><strong>参数讲解</strong>：</p>
<ul>
<li><p>params:表示完成的嵌入张量，或者除了第一维度之外具有相同形状的P个张量的列表，表示经分割的嵌入张量。</p>
</li>
<li><p>ids:一个类型为int32或int64的Tensor，包含要在params中查找的id。</p>
</li>
<li><p>partiton_strategy:指定分区策略的字符串，如果len（params）&gt;1，则相关。当前支持“div”和”mod”。默认为”mod”。</p>
</li>
<li><p>name:操作可选。</p>
</li>
<li><p>validate_indices:是否验证收集索引。</p>
</li>
<li><p>max_norm：如果不是None,嵌入值将被归一化为max_norm的值。</p>
</li>
</ul>
<p><strong>代码示例如下：</strong></p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf
<span class="token keyword">import</span> numpy <span class="token keyword">as</span> np
a <span class="token operator">=</span> np<span class="token punctuation">.</span>random<span class="token punctuation">.</span>random<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token number">5</span><span class="token punctuation">,</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
b <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>embedding_lookup<span class="token punctuation">(</span>a<span class="token punctuation">,</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span><span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
<span class="token keyword">with</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span> <span class="token keyword">as</span> sess<span class="token punctuation">:</span>
    sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>initialize_all_variables<span class="token punctuation">(</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"经过查找的数组：\n"</span><span class="token punctuation">,</span>sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>b<span class="token punctuation">)</span><span class="token punctuation">)</span>
    <span class="token keyword">print</span><span class="token punctuation">(</span><span class="token string">"输出全部的数组:\n"</span><span class="token punctuation">,</span>a<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p>输出的结果如下：</p>
<p><img src="/Images/Tensorflow02/image-20210406215349239.png" alt="image-20210406215349239"></p>
<h1 id="三、tf-shape-a-和a-get-shape的区别"><a href="#三、tf-shape-a-和a-get-shape的区别" class="headerlink" title="三、tf.shape(a)和a.get_shape的区别"></a>三、tf.shape(a)和a.get_shape的区别</h1><p><strong>两者之间的比较</strong>：</p>
<ul>
<li><p>相同点:都可以得到tensor a 的尺寸</p>
</li>
<li><p>不同点：tf.shape(a)中a数据的类型可以是tensor,list,array，tuple。而a.get_shape()中的<strong>a的数据类型必须是tensor</strong>，<strong>且返回的是一个数组</strong>，可通过a.get_shape.as_list()得到一个列表。</p>
</li>
</ul>
<p><strong>tf.shape(a)的代码示例如下：</strong></p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf
<span class="token keyword">import</span> numpy <span class="token keyword">as</span> np

x <span class="token operator">=</span> tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>
y <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span>

sess <span class="token operator">=</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token comment" spellcheck="true"># tf.shape()</span>
x_shape <span class="token operator">=</span> tf<span class="token punctuation">.</span>shape<span class="token punctuation">(</span>x<span class="token punctuation">)</span>  <span class="token comment" spellcheck="true"># x_shape 是一个tensor</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>x_shape<span class="token punctuation">)</span>       <span class="token comment" spellcheck="true"># Tensor("Shape:0",shape=(2,),dtype=int32)</span>
y_shape <span class="token operator">=</span> tf<span class="token punctuation">.</span>shape<span class="token punctuation">(</span>y<span class="token punctuation">)</span>  <span class="token comment" spellcheck="true"># &lt;tf.Tensor 'Shape_2:0' shape=(2,) dtype=int32></span>
<span class="token keyword">print</span><span class="token punctuation">(</span>sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>x_shape<span class="token punctuation">)</span><span class="token punctuation">)</span>  <span class="token comment" spellcheck="true"># 如需获得tensor的值，则将其放入sess.run() 中查看</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>y_shape<span class="token punctuation">)</span><span class="token punctuation">)</span>  
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p><strong>下面三个</strong>输出结果如下：</p>
<p><img src="/Images/Tensorflow02/image-20210407102946786.png"></p>
<p><strong>a.get_shape的代码如下：</strong></p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">import</span> tensorflow <span class="token keyword">as</span> tf

x <span class="token operator">=</span> tf<span class="token punctuation">.</span>constant<span class="token punctuation">(</span><span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span><span class="token punctuation">)</span>   <span class="token comment" spellcheck="true"># x是一个tensor</span>
y <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token punctuation">[</span><span class="token number">4</span><span class="token punctuation">,</span> <span class="token number">5</span><span class="token punctuation">,</span> <span class="token number">6</span><span class="token punctuation">]</span><span class="token punctuation">]</span>                <span class="token comment" spellcheck="true"># y是一个列表</span>

sess <span class="token operator">=</span> tf<span class="token punctuation">.</span>Session<span class="token punctuation">(</span><span class="token punctuation">)</span>

x_shape <span class="token operator">=</span> x<span class="token punctuation">.</span>get_shape<span class="token punctuation">(</span><span class="token punctuation">)</span>
y_shape <span class="token operator">=</span> y<span class="token punctuation">.</span>get_shape<span class="token punctuation">(</span><span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>x_shape<span class="token punctuation">)</span>
<span class="token keyword">print</span><span class="token punctuation">(</span>y_shape<span class="token punctuation">)</span>     
<span class="token keyword">print</span><span class="token punctuation">(</span>sess<span class="token punctuation">.</span>run<span class="token punctuation">(</span>x_shape<span class="token punctuation">)</span><span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></code></pre>
<p>x<strong>的输出结果</strong>如下：由此可知：a.get_shape()返回的是一个<strong>数组</strong>。</p>
<p><img src="/Images/Tensorflow02/image-20210406175746995.png" alt="image-20210406175746995"><img src="/Images/Tensorflow02/image-20210406175810868.png" alt="image-20210406175810868"></p>
<p>y<strong>的输出结果</strong>如下：与上述x比较，可知：a.get_shape()中的<strong>a的数据类型必须是tensor</strong>。</p>
<p><img src="/Images/Tensorflow02/image-20210406180950175.png" alt="image-20210406180950175"></p>
<h1 id="四、tf-nn-dropout（防止过拟合）"><a href="#四、tf-nn-dropout（防止过拟合）" class="headerlink" title="四、tf.nn.dropout（防止过拟合）"></a>四、tf.nn.dropout（防止过拟合）</h1><p><strong>一、简述：</strong>机器学习中，过拟合是一种常见的问题。过拟合指的是只能你和训练数据，但不能很好的拟合不包含在训练数据中的其他数据的状态。机器学习的目标是提高泛化能力，即便是灭有包含在训练数据里的未观测的数据，也希望模型能够进行正确的识别。我们可以制作复杂的、表现力强的模型。发生过拟合的原因，主要有一下两个原因：</p>
<ul>
<li><p>模型拥有大量参数、表现力强。</p>
</li>
<li><p>训练数据少。</p>
<p><img src="/Images/Tensorflow02/image-20210407210314873.png" alt="image-20210407210314873"></p>
</li>
</ul>
<p><strong>二、抑制过拟合的方法：</strong></p>
<ul>
<li>权值衰减:该方法通过在学习中对大的权重进行惩罚来抑制过拟合。很多过拟合原本就是因为权重参数取值过大造成的。如为损失函数加上权重的平方范数（L2范数）。在这里面，为损失函数加上<br>$$<br>\frac{1}{2}\lambda w^2<br>$$</li>
<li>Dropout:上述权值衰减只能在某种程度上抑制过拟合，如果网络的模型变得很复杂，只用权值衰减就难以应付，所以我们通常会考虑Dropout。Dropout是一种在学习过程中随机删除神经元的方法。训练时，随机选出隐藏层的神经元，然后将其删除，不再进行信号的传递。也不是真正意义上的删除，而是在这一轮中不更新这个神经元的权值，不参加神经网络的计算，权值在这一轮训练被保留，下一轮训练可能会被重新更新。注：测试时，对于每个神经元的输出，要乘上训练时的删除比例后再输出。</li>
</ul>
<p>三、tensorflow中有两个dropout函数容易混淆</p>
<ul>
<li><p>tf.nn.dropout中参数keep_drop：每一个元素被保存下来的概率。</p>
<p>函数介绍：</p>
<pre class="line-numbers language-python"><code class="language-python"><span class="token keyword">def</span> <span class="token function">dropout</span><span class="token punctuation">(</span>x<span class="token punctuation">,</span> keep_prob<span class="token punctuation">,</span> noise_shape<span class="token operator">=</span>None<span class="token punctuation">,</span> seed<span class="token operator">=</span>None<span class="token punctuation">,</span> name<span class="token operator">=</span>None<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<p>参数讲解：</p>
<ul>
<li><p>x:上一层传下的tensor。</p>
</li>
<li><p>keep_prob:每一个神经元被保留下来的概率。保留keep_prob的神经元继续工作，其余的停止工作与更新。（在这里并不是真正被丢掉，而是在这一轮的训练中不更新这个神经元的权值，权值在这一轮训练被保留，下一轮训练可能又会被更新。）</p>
</li>
<li><p>seed:整形变量，随机种子。</p>
</li>
<li><p>name:指定dropout操作的名字。</p>
<p><strong>注意</strong>:</p>
<ul>
<li><p>dropout必须设置概率keep_prob,keep_prob应初始化为占位符placeholder。定义如下：</p>
<pre class="line-numbers language-python"><code class="language-python">keep_prob <span class="token operator">=</span> tf<span class="token punctuation">.</span>placeholder<span class="token punctuation">(</span>tf<span class="token punctuation">.</span>float32<span class="token punctuation">)</span>  
h_drop <span class="token operator">=</span> tf<span class="token punctuation">.</span>nn<span class="token punctuation">.</span>dropout<span class="token punctuation">(</span>encoded<span class="token punctuation">,</span> keep_prob<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span></span></code></pre>
</li>
<li><p>train的时候才是dropout起作用的时候,test的时候不应该让dropout起作用。</p>
</li>
</ul>
</li>
</ul>
</li>
<li><p>tf.layer.dropout中参数rate：每一个元素被丢弃的概率。keep_prob = 1-rate。</p>
<p>函数介绍：</p>
<pre class="line-numbers language-python"><code class="language-python">tf<span class="token punctuation">.</span>layers<span class="token punctuation">.</span>dropout<span class="token punctuation">(</span>inputs<span class="token punctuation">,</span>rate<span class="token operator">=</span><span class="token number">0.5</span><span class="token punctuation">,</span>noise_shape<span class="token operator">=</span>None<span class="token punctuation">,</span>seed<span class="token operator">=</span>None<span class="token punctuation">,</span>training<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">,</span>name<span class="token operator">=</span>None<span class="token punctuation">)</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span></span></code></pre>
<p> 参数讲解：</p>
<ul>
<li>training 参数：在training=True时，返回应用dropout后的输出；在training=False时，正常返回输出（没有dropout）。一般在training过程training=true,即启动dropout，在每次迭代都rate比例的神经元。</li>
</ul>
</li>
</ul>

                
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                                var start = first_occur - 20;
                                var end = first_occur + 80;
                                if (start < 0) {
                                    start = 0;
                                }
                                if (start === 0) {
                                    end = 100;
                                }
                                if (end > content.length) {
                                    end = content.length;
                                }
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                                // highlight all keywords
                                keywords.forEach(function (keyword) {
                                    var regS = new RegExp(keyword, "gi");
                                    match_content = match_content.replace(regS, "<em class=\"search-keyword\">" + keyword + "</em>");
                                });

                                str += "<p class=\"search-result\">" + match_content + "...</p>"
                            }
                            str += "</li>";
                        }
                    });
                    str += "</ul>";
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                });
            }
        });
    };

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});
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